{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example of using ST-DBSCAN "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from st_dbscan import ST_DBSCAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>animal_id</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>201.623002</td>\n",
       "      <td>186.148565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>247.658670</td>\n",
       "      <td>177.650135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>127.457230</td>\n",
       "      <td>20.572136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>231.180722</td>\n",
       "      <td>172.861323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>217.739517</td>\n",
       "      <td>189.203565</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   time  animal_id           x           y\n",
       "0     0          0  201.623002  186.148565\n",
       "1     0          1  247.658670  177.650135\n",
       "2     0          2  127.457230   20.572136\n",
       "3     0          3  231.180722  172.861323\n",
       "4     0          4  217.739517  189.203565"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('test-data.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# normalize the data \n",
    "df['x'] = (df['x'] - df['x'].min()) / (df['x'].max() - df['x'].min())\n",
    "df['y'] = (df['y'] - df['y'].min()) / (df['y'].max() - df['y'].min())\n",
    "# transform to numpy array\n",
    "data = df.loc[:, ['time','x','y']].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The input data format is: ```['temporal_index','x','y', <optional attributes>]```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "st_dbscan = ST_DBSCAN(eps1 = 0.05, eps2 = 10, min_samples = 5) \n",
    "# for list of all parameter description see - src code comments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<st_dbscan.st_dbscan.ST_DBSCAN at 0x28567d4ada0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st_dbscan.fit(data) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot(data, labels):\n",
    "    colors = ['#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f','#ff7f00','#cab2d6','#6a3d9a']\n",
    "    \n",
    "    for i in range(-1, len(set(labels))):\n",
    "        if i == -1:\n",
    "            col = [0, 0, 0, 1]\n",
    "        else:\n",
    "            col = colors[i % len(colors)]\n",
    "        \n",
    "        clust = data[np.where(labels==i)]\n",
    "        plt.scatter(clust[:,0], clust[:,1], c=[col], s=1)\n",
    "    plt.show()\n",
    "\n",
    "    return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(data[:,1:], st_dbscan.labels) "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
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